"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class 			UpperCamelCase						(     lowercase_		, unittest.TestCase    ):
   lowercase				=    KandinskyVaaImgaImgPipeline
   lowercase				=    ["""image_embeds""", """negative_image_embeds""", """image"""]
   lowercase				=    [
       """image_embeds""",
       """negative_image_embeds""",
       """image""",
   ]
   lowercase				=    [
       """generator""",
       """height""",
       """width""",
       """strength""",
       """guidance_scale""",
       """num_inference_steps""",
       """return_dict""",
       """guidance_scale""",
       """num_images_per_prompt""",
       """output_type""",
       """return_dict""",
   ]
   lowercase				=    False
   @property
   def      _UpperCAmelCase  (     self     )      -> int:


        '''simple docstring'''


        return 32
   @property
   def      _UpperCAmelCase  (     self     )      -> Any:


        '''simple docstring'''


        return 32
   @property
   def      _UpperCAmelCase  (     self     )      -> List[str]:


        '''simple docstring'''


        return self.time_input_dim
   @property
   def      _UpperCAmelCase  (     self     )      -> List[str]:


        '''simple docstring'''


        return self.time_input_dim * 4
   @property
   def      _UpperCAmelCase  (     self     )      -> Any:


        '''simple docstring'''


        return 100
   @property
   def      _UpperCAmelCase  (     self     )      -> List[Any]:


        '''simple docstring'''


        torch.manual_seed(0     )

        lowercase_      :							Dict						=      {
            'in_channels': 4,
            # Out channels is double in channels because predicts mean and variance
            'out_channels': 8,
            'addition_embed_type': 'image',
            'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
            'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
            'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
            'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
            'layers_per_block': 1,
            'encoder_hid_dim': self.text_embedder_hidden_size,
            'encoder_hid_dim_type': 'image_proj',
            'cross_attention_dim': self.cross_attention_dim,
            'attention_head_dim': 4,
            'resnet_time_scale_shift': 'scale_shift',
            'class_embed_type': None,
        }

        lowercase_      :							Optional[int]						=      UNetaDConditionModel(**__UpperCamelCase     )
        return model
   @property
   def      _UpperCAmelCase  (     self     )      -> List[Any]:


        '''simple docstring'''


        return {
            "block_out_channels": [32, 64],
            "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
            "in_channels": 3,
            "latent_channels": 4,
            "layers_per_block": 1,
            "norm_num_groups": 8,
            "norm_type": "spatial",
            "num_vq_embeddings": 12,
            "out_channels": 3,
            "up_block_types": [
                "AttnUpDecoderBlock2D",
                "UpDecoderBlock2D",
            ],
            "vq_embed_dim": 4,
        }
   @property
   def      _UpperCAmelCase  (     self     )      -> int:


        '''simple docstring'''


        torch.manual_seed(0     )
        lowercase_      :							int						=      VQModel(**self.dummy_movq_kwargs     )
        return model
   def      _UpperCAmelCase  (     self     )      -> Dict:


        '''simple docstring'''


        lowercase_      :							Any						=      self.dummy_unet
        lowercase_      :							List[str]						=      self.dummy_movq

        lowercase_      :							Dict						=      {
            'num_train_timesteps': 1000,
            'beta_schedule': 'linear',
            'beta_start': 0.0_0085,
            'beta_end': 0.012,
            'clip_sample': False,
            'set_alpha_to_one': False,
            'steps_offset': 0,
            'prediction_type': 'epsilon',
            'thresholding': False,
        }

        lowercase_      :							Union[str, Any]						=      DDIMScheduler(**__UpperCamelCase     )

        lowercase_      :							Dict						=      {
            'unet': unet,
            'scheduler': scheduler,
            'movq': movq,
        }

        return components
   def      _UpperCAmelCase  (     self	,__UpperCamelCase	,__UpperCamelCase=0     )      -> Tuple:


        '''simple docstring'''


        lowercase_      :							int						=      floats_tensor((1, self.text_embedder_hidden_size)	,rng=random.Random(__UpperCamelCase     )     ).to(__UpperCamelCase     )
        lowercase_      :							Dict						=      floats_tensor((1, self.text_embedder_hidden_size)	,rng=random.Random(seed + 1     )     ).to(
            __UpperCamelCase     )
        # create init_image
        lowercase_      :							Union[str, Any]						=      floats_tensor((1, 3, 64, 64)	,rng=random.Random(__UpperCamelCase     )     ).to(__UpperCamelCase     )
        lowercase_      :							Tuple						=      image.cpu().permute(0	,2	,3	,1     )[0]
        lowercase_      :							List[str]						=      Image.fromarray(np.uinta(__UpperCamelCase     )     ).convert('RGB'     ).resize((256, 256)     )

        if str(__UpperCamelCase     ).startswith('mps'     ):
             lowercase_      :							int						=      torch.manual_seed(__UpperCamelCase     )
        else:
             lowercase_      :							Optional[Any]						=      torch.Generator(device=__UpperCamelCase     ).manual_seed(__UpperCamelCase     )
        lowercase_      :							Optional[int]						=      {
            'image': init_image,
            'image_embeds': image_embeds,
            'negative_image_embeds': negative_image_embeds,
            'generator': generator,
            'height': 64,
            'width': 64,
            'num_inference_steps': 10,
            'guidance_scale': 7.0,
            'strength': 0.2,
            'output_type': 'np',
        }
        return inputs







   def      _UpperCAmelCase  (     self     )      -> List[str]:


        '''simple docstring'''


        lowercase_      :							Dict						=      'cpu'

        lowercase_      :							str						=      self.get_dummy_components()

        lowercase_      :							List[str]						=      self.pipeline_class(**__UpperCamelCase     )
        lowercase_      :							Tuple						=      pipe.to(__UpperCamelCase     )

        pipe.set_progress_bar_config(disable=__UpperCamelCase     )

        lowercase_      :							Dict						=      pipe(**self.get_dummy_inputs(__UpperCamelCase     )     )
        lowercase_      :							str						=      output.images

        lowercase_      :							List[str]						=      pipe(
            **self.get_dummy_inputs(__UpperCamelCase     )	,return_dict=__UpperCamelCase	,)[0]

        lowercase_      :							Optional[Any]						=      image[0, -3:, -3:, -1]
        lowercase_      :							Optional[Any]						=      image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        lowercase_      :							Any						=      np.array(
            [0.619_9778, 0.6398_4406, 0.4614_5785, 0.6294_4984, 0.562_2215, 0.4730_6132, 0.4744_1456, 0.460_7606, 0.4871_9263]     )
        assert (
            np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2
        ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
        assert (
            np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1e-2
        ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''







@slow
@require_torch_gpu
class 			UpperCamelCase						(     unittest.TestCase    ):
   def      _UpperCAmelCase  (     self     )      -> List[str]:


        '''simple docstring'''


        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







   def      _UpperCAmelCase  (     self     )      -> int:


        '''simple docstring'''


        lowercase_      :							str						=      load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinskyv22/kandinskyv22_img2img_frog.npy'     )

        lowercase_      :							int						=      load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'     )
        lowercase_      :							Optional[int]						=      'A red cartoon frog, 4k'

        lowercase_      :							Union[str, Any]						=      KandinskyVaaPriorPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-2-prior'	,torch_dtype=torch.floataa     )
        pipe_prior.to(__UpperCamelCase     )

        lowercase_      :							str						=      KandinskyVaaImgaImgPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-2-decoder'	,torch_dtype=torch.floataa     )
        lowercase_      :							Optional[Any]						=      pipeline.to(__UpperCamelCase     )

        pipeline.set_progress_bar_config(disable=__UpperCamelCase     )

        lowercase_      :							Optional[int]						=      torch.Generator(device='cpu'     ).manual_seed(0     )
        lowercase_      :							Tuple						=      pipe_prior(
            __UpperCamelCase	,generator=__UpperCamelCase	,num_inference_steps=5	,negative_prompt=''	,).to_tuple()

        lowercase_      :							Tuple						=      pipeline(
            image=__UpperCamelCase	,image_embeds=__UpperCamelCase	,negative_image_embeds=__UpperCamelCase	,generator=__UpperCamelCase	,num_inference_steps=100	,height=768	,width=768	,strength=0.2	,output_type='np'	,)

        lowercase_      :							Tuple						=      output.images[0]

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(__UpperCamelCase	,__UpperCamelCase     )









"""simple docstring"""


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
		from PIL import Image




class 						__UpperCamelCase     (     UpperCamelCase						):
					lowercase_    :				Optional[Any]      =  """Salesforce/blip-image-captioning-base"""
					lowercase_    :				Optional[int]      =  (
					    """This is a tool that generates a description of an image. It takes an input named `image` which should be the """
					    """image to caption, and returns a text that contains the description in English."""
					)
					lowercase_    :				int      =  """image_captioner"""
					lowercase_    :				int      =  AutoModelForVisionaSeq

					lowercase_    :				List[str]      =  ["""image"""]
					lowercase_    :				int      =  ["""text"""]
					def __init__(							self							:	Any     , *UpperCAmelCase							:	str     , **UpperCAmelCase							:	List[str]   )	->       Union[str, Any]:
											requires_backends(self     , ['vision']   )
											super().__init__(*UpperCAmelCase     , **UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	int     , UpperCAmelCase							:	"Image"   )	->       Union[str, Any]:
											return self.pre_processor(images=UpperCAmelCase     , return_tensors='pt'   )
					def        UpperCAmelCase__ (							self							:	int     , UpperCAmelCase							:	Optional[int]   )	->       Optional[int]:
											return self.model.generate(**UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Dict     , UpperCAmelCase							:	int   )	->       Dict:
											return self.pre_processor.batch_decode(UpperCAmelCase     , skip_special_tokens=UpperCAmelCase   )[0].strip()